print "Performing greedy feature selection..."score_hist = []N = 10good_features = set([])# Greedy feature selection loopwhile len(score_hist) < 2 or score_hist[-1][0] > score_hist[-2][0]:    scores = []    for f in range(len(Xts)):        if f not in good_features:            feats = list(good_features) + [f]            Xt = sparse.hstack([Xts[j] for j in feats]).tocsr()            score = cv_loop(Xt, y, model, N)            scores.append((score, f))            print "Feature: %i Mean AUC: %f" % (f, score)    good_features.add(sorted(scores)[-1][1])    score_hist.append(sorted(scores)[-1])    print "Current features: %s" % sorted(list(good_features))

注意还没结束：

# Remove last added feature from good_featuresgood_features.remove(score_hist[-1][1])

from kaggle